R Notebook

library(Seurat)
library(monocle)

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    'citation("Biobase")', and for packages 'citation("pkgname")'.

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library(dplyr)

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library(ggplot2)
library(ggrepel)
library(GOplot)

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Loading required package: RColorBrewer

seurobj <- readRDS('../output/seurat_objects/180831/10x-180831')
cds <- readRDS('output/monocle/180831/10x-180831-monocle-monocle_genelist_T1T2T3_T4T5_res.1.5')

cannot open compressed file 'output/monocle/180831/10x-180831-monocle-monocle_genelist_T1T2T3_T4T5_res.1.5', probable reason 'No such file or directory'Error in gzfile(file, "rb") : cannot open the connection

Figure 1

rr save_plot(../figures/figures_paper/main_figures/figure1/10x-180831_tsne_depots.pdf, p1, base_width=6, base_height=4.5) save_plot(../figures/figures_paper/main_figures/figure1/10x-180831_tsne_timepoints.pdf, p2, base_width=4, base_height=3) r save_plot(../figures/figures_paper/main_figures/figure1/10x-180831_tsne_t1_aligned.pdf, p3, base_width=6, base_height=4.5)

Figure 2

Figure 3

Heatmap

Heatmap

rr load(‘../output/monocle/180831/heatmaps’) heatmap <- heatmaps\(heatmap_logFC0.3_ncluster6_filtered_genes gridExtra::grid.arrange(heatmap\)ph_res$gtable)

rr save_plot(../figures/figures_paper/main_figures/figure3/BEAM_heatmap_logFC0.3_ncluster6_filtered_genes.pdf, heatmap\(ph_res\)gtable, base_width=7, base_height=8)

Pseudotime plots

Pseudotime plots

rr #new_cds <- buildBranchCellDataSet(cds, progenitor_method=‘duplicate’, branch_states=c(2,3), branch_point=1, branch_labels=c(‘Metabolic’, ‘ECM’), stretch=T) #saveRDS(new_cds, file=‘../output/monocle/180831/10x-180831-monocle-monocle_genelist_T1T2T3_T4T5_res.1.5_duplicated-progenitor_stretched-pseudotime’) #new_cds <- readRDS(‘../output/monocle/180831/10x-180831-monocle-monocle_genelist_T1T2T3_T4T5_res.1.5_duplicated-progenitor_stretched-pseudotime’) #new cds contains duplicated progenitor cells (one assigned to each branch) and stretched Pseudotime df_pdata <- pData(new_cds)[c(‘original_cell_id’, ‘Branch’, ‘State.labels’, ‘Pseudotime’)]

Error in pData(new_cds) : object 'new_cds' not found

rr width=6 height=5 save_plot(../figures/figures_paper/main_figures/figure3/pseudotime_ADIPOQ.pdf, adipoq_pt, base_width=width, base_height=height) save_plot(../figures/figures_paper/main_figures/figure3/pseudotime_UCP2.pdf, ucp2_pt, base_width=width, base_height=height) r save_plot(../figures/figures_paper/main_figures/figure3/pseudotime_DCN.pdf, dcn_pt, base_width=width, base_height=height) save_plot(../figures/figures_paper/main_figures/figure3/pseudotime_APOD.pdf, apod_pt, base_width=width, base_height=height)

Featureplots

genes <- c('ADIPOQ', 'UCP2', 'DCN', 'APOD')
featureplots_noleg <- FeaturePlot(seurobj, features.plot=genes, cols.use=c('gray', 'blue'), no.legend=T, do.return=T, no.axes=T, pt.size=1, max.cutoff=c(5)) 

featureplots_leg <- FeaturePlot(seurobj, features.plot=genes, cols.use=c('gray', 'blue'), no.legend=F, do.return=T, no.axes=T, pt.size=1, max.cutoff=c(5)) 

adipoq_fp <- featureplots_noleg$ADIPOQ + theme(plot.title=element_blank())
ucp2_fp <- featureplots$UCP2 + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar', limits=c(0,5)) + theme(plot.title=element_blank(), legend.title=element_text(size=20), legend.text=element_text(size=20), legend.key.height = unit(1.3, 'cm'))

save_plot("../figures/figures_paper/main_figures/Figure3_beam/featureplots_metabolic.pdf", metabolic_fp, base_width=width, base_height=height)

Error in save_plot("../figures/figures_paper/main_figures/Figure3_beam/featureplots_metabolic.pdf",  : 
  object 'height' not found

GO bubble plot

name <- 'heatmap_logFC0.3_ncluster6'
folder <- paste('../tables/tables_paper/all_tables/BEAM/', name, sep='')
#clusters <- c(1,2,3,4,6)
clusters <- c(2,6,3)
gsea <- list()
genelist <- list()
for (i in clusters){
  gsea_cluster <- read.table(paste(folder, '/REVIGO_cleaned/', 'REVIGO_cluster', i, '_cleaned.tsv', sep=''), header=T)
  gsea_cluster['cluster'] <- i
  genes_cluster <- read.table(paste(folder, '/genelist_cluster', i, '.tsv', sep=''), header=T)
  gsea[[i]] <- gsea_cluster
  genelist[[i]] <- genes_cluster
}
gsea <- do.call("rbind", gsea)
genelist <- do.call("rbind", genelist)
create_terms <- function(gsea){
  return(data.frame(category=gsea$gsea.domain, ID=gsea$revigo.term_ID, term=gsea$revigo.description, adj_pval=gsea$gsea.p.value, genes=gsea$gsea.intersection, cluster=gsea$cluster))
}
create_genes <- function(genelist){
  return(data.frame(ID=genelist$gene_short_name, logFC=genelist$avgLogFC_State2_State3))
}
terms <- create_terms(gsea)
genes <- create_genes(genelist)
circ <- circle_dat(terms, genes)

rr save_plot(‘../figures/figures_paper/main_figures/Figure3_beam/180831_beamGO_all-terms.pd_labels3_zscore4.5.pdf’, p, base_width=20, base_height=7)

Percent.mito

rr percent_mito_fp <- FeaturePlot(seurobj, features.plot=c(‘percent.mito.100’), cols.use=c(‘gray’, ‘blue’), no.legend=F, do.return=T, no.axes=T, max.cutoff = c(10))$percent.mito + ggtitle(‘’) + scale_color_gradient(name=’% mitochondrialexpression’, low=‘gray’, high=‘blue’, guide=‘colorbar’) + theme(legend.key.height = unit(1.6, ‘cm’))

rr save_plot(../figures/figures_paper/main_figures/figure3/featureplot_PERCENT_MITO.pdf, percent_mito_fp, base_width=width, base_height=height)

BATLAS

rr batlas_results <- read.table(‘../tables/tables_paper/supplementary_tables/BATLAS/BATLAS_branch_high_res.txt’, sep=‘’, header=T) batlas_results[‘pseudotime_decile’] <- seurobj@meta.data$pseudotime_decile[match(batlas_results\(sample, seurobj@meta.data\)branch_high_res)] batlas_results[‘branch’] <- seurobj@meta.data$branch[match(batlas_results\(sample, seurobj@meta.data\)branch_high_res)] batlas_results <- batlas_results %>% mutate(block_branch = if_else(branch == ‘Progenitor’, ‘Beginning’, ‘End’)) batlas_results[‘brown_centered’] <- batlas_results\(brown - 0.5 batlas_results['ratio'] <- batlas_results\)brown/batlas_results$white

rr p <- ggplot(batlas_results, aes(x=as.factor(pseudotime_decile), y=brown_centered, fill=branch)) + geom_col(position = position_dodge2(width=0.9, preserve=‘single’)) + labs(x=, fill=, y=NULL) + scale_fill_manual(values=colormap.branches, labels=c(‘L’, ‘U’, ‘P’)) + facet_grid(cols=vars(block_branch), switch=‘x’) + scale_y_continuous(limits=c(-0.5, 0.5), breaks=seq(-0.5, 0.5, by=0.5), labels=c(‘100% white’, ‘50/50’, ‘100% brown’)) + theme(strip.background=element_blank(), axis.text.x=element_blank(), strip.placement=‘outside’) p

rr save_plot(‘../figures/figures_paper/main_figures/figure3/BATLAS_results.pdf’, p, base_height=5, base_width=8)

SOM Figures

Clusters

TF analysis

rr fig <- plot_grid( TSNEPlot(seurobj, group.by=‘res.0.5’, pt.size=pt.size.tsne, do.label=T), TSNEPlot(seurobj, group.by=‘res.0.7’, pt.size=pt.size.tsne, do.label=T), TSNEPlot(seurobj, group.by=‘res.1’, pt.size=pt.size.tsne, do.label=T), TSNEPlot(seurobj, group.by=‘res.1.5’, pt.size=pt.size.tsne, do.label=T), ncol=2, labels=‘auto’ )

Metabolic and ECM genes

rr FeaturePlot(seurobj, features.plot=‘Pseudotime’)